{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 高中体测数据可视化（体测分数_男生，体测分数-女生）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对男1000米跑、男引体进行等宽分箱操作，分成3份，并使用饼图绘制百分比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore') # 忽略警告"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>班级</th>\n",
       "      <th>性别</th>\n",
       "      <th>男1000米跑</th>\n",
       "      <th>男1000米跑分数</th>\n",
       "      <th>男50米跑</th>\n",
       "      <th>男50米跑分数</th>\n",
       "      <th>男跳远</th>\n",
       "      <th>男跳远分数</th>\n",
       "      <th>男体前屈</th>\n",
       "      <th>男体前屈分数</th>\n",
       "      <th>男引体</th>\n",
       "      <th>男引体分数</th>\n",
       "      <th>男肺活量</th>\n",
       "      <th>男肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.13</td>\n",
       "      <td>72</td>\n",
       "      <td>8.88</td>\n",
       "      <td>66</td>\n",
       "      <td>195</td>\n",
       "      <td>60</td>\n",
       "      <td>12</td>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2785</td>\n",
       "      <td>62</td>\n",
       "      <td>170</td>\n",
       "      <td>72.599998</td>\n",
       "      <td>25.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.16</td>\n",
       "      <td>70</td>\n",
       "      <td>7.70</td>\n",
       "      <td>78</td>\n",
       "      <td>225</td>\n",
       "      <td>74</td>\n",
       "      <td>11</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>60</td>\n",
       "      <td>3133</td>\n",
       "      <td>68</td>\n",
       "      <td>174</td>\n",
       "      <td>52.700001</td>\n",
       "      <td>17.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.09</td>\n",
       "      <td>74</td>\n",
       "      <td>8.45</td>\n",
       "      <td>70</td>\n",
       "      <td>218</td>\n",
       "      <td>70</td>\n",
       "      <td>14</td>\n",
       "      <td>78</td>\n",
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       "      <td>3901</td>\n",
       "      <td>80</td>\n",
       "      <td>169</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>16.280001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>4.21</td>\n",
       "      <td>68</td>\n",
       "      <td>8.05</td>\n",
       "      <td>74</td>\n",
       "      <td>206</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4946</td>\n",
       "      <td>100</td>\n",
       "      <td>183</td>\n",
       "      <td>79.699997</td>\n",
       "      <td>23.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>男</td>\n",
       "      <td>3.44</td>\n",
       "      <td>85</td>\n",
       "      <td>7.52</td>\n",
       "      <td>78</td>\n",
       "      <td>210</td>\n",
       "      <td>66</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "      <td>9</td>\n",
       "      <td>68</td>\n",
       "      <td>3538</td>\n",
       "      <td>74</td>\n",
       "      <td>171</td>\n",
       "      <td>54.700001</td>\n",
       "      <td>18.709999</td>\n",
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      "text/plain": [
       "   班级 性别  男1000米跑  男1000米跑分数  男50米跑  男50米跑分数  男跳远  男跳远分数  男体前屈  男体前屈分数  男引体  \\\n",
       "0   1  男     4.13         72   8.88       66  195     60    12      74    1   \n",
       "1   1  男     4.16         70   7.70       78  225     74    11      74    7   \n",
       "2   1  男     4.09         74   8.45       70  218     70    14      78    1   \n",
       "3   1  男     4.21         68   8.05       74  206     64    13      76    1   \n",
       "4   1  男     3.44         85   7.52       78  210     66    13      76    9   \n",
       "\n",
       "   男引体分数  男肺活量  男肺活量分数   身高         体重        BMI  \n",
       "0      0  2785      62  170  72.599998  25.120001  \n",
       "1     60  3133      68  174  52.700001  17.410000  \n",
       "2      0  3901      80  169  46.500000  16.280001  \n",
       "3      0  4946     100  183  79.699997  23.799999  \n",
       "4     68  3538      74  171  54.700001  18.709999  "
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_score = pd.read_excel('./体测分数_男生.xls')\n",
    "male_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>女800米跑</th>\n",
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       "      <th>女50米跑</th>\n",
       "      <th>女50米跑分数</th>\n",
       "      <th>女跳远</th>\n",
       "      <th>女跳远分数</th>\n",
       "      <th>女体前屈</th>\n",
       "      <th>女体前屈分数</th>\n",
       "      <th>女仰卧</th>\n",
       "      <th>女仰卧分数</th>\n",
       "      <th>女肺活量</th>\n",
       "      <th>女肺活量分数</th>\n",
       "      <th>身高</th>\n",
       "      <th>体重</th>\n",
       "      <th>BMI</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.22</td>\n",
       "      <td>100</td>\n",
       "      <td>9.32</td>\n",
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       "      <td>85</td>\n",
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       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>19.309999</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>4.59</td>\n",
       "      <td>40</td>\n",
       "      <td>11.44</td>\n",
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       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "      <td>3683</td>\n",
       "      <td>100</td>\n",
       "      <td>163.0</td>\n",
       "      <td>66.599998</td>\n",
       "      <td>25.070000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.46</td>\n",
       "      <td>80</td>\n",
       "      <td>13.40</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>60</td>\n",
       "      <td>7</td>\n",
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       "      <td>40</td>\n",
       "      <td>76</td>\n",
       "      <td>3331</td>\n",
       "      <td>100</td>\n",
       "      <td>157.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>24.340000</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.39</td>\n",
       "      <td>85</td>\n",
       "      <td>9.52</td>\n",
       "      <td>70</td>\n",
       "      <td>172</td>\n",
       "      <td>76</td>\n",
       "      <td>21</td>\n",
       "      <td>90</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>3701</td>\n",
       "      <td>100</td>\n",
       "      <td>160.0</td>\n",
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       "      <td>19.799999</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>3.43</td>\n",
       "      <td>80</td>\n",
       "      <td>9.79</td>\n",
       "      <td>68</td>\n",
       "      <td>145</td>\n",
       "      <td>50</td>\n",
       "      <td>8</td>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>70</td>\n",
       "      <td>3592</td>\n",
       "      <td>100</td>\n",
       "      <td>167.0</td>\n",
       "      <td>63.900002</td>\n",
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      "text/plain": [
       "   班级 性别  女800米跑  女800米跑分数  女50米跑  女50米跑分数  女跳远  女跳远分数  女体前屈  女体前屈分数  女仰卧  \\\n",
       "0   1  女    3.22       100   9.32       72  185     85    16      76   48   \n",
       "1   1  女    4.59        40  11.44       10  148     60     9      66   29   \n",
       "2   1  女    3.46        80  13.40        0  150     60     7      64   40   \n",
       "3   1  女    3.39        85   9.52       70  172     76    21      90   46   \n",
       "4   1  女    3.43        80   9.79       68  145     50     8      64   34   \n",
       "\n",
       "   女仰卧分数  女肺活量  女肺活量分数     身高         体重        BMI  \n",
       "0     85  3775     100  163.0  51.299999  19.309999  \n",
       "1     66  3683     100  163.0  66.599998  25.070000  \n",
       "2     76  3331     100  157.0  60.000000  24.340000  \n",
       "3     85  3701     100  160.0  50.700001  19.799999  \n",
       "4     70  3592     100  167.0  63.900002  22.910000  "
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "female_score = pd.read_excel('./体测分数_女生.xls')\n",
    "female_score.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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       " 'Segoe UI',\n",
       " 'STZhongsong',\n",
       " 'Droid Serif',\n",
       " 'Bookman Old Style',\n",
       " 'Marlett',\n",
       " 'Bookman Old Style',\n",
       " 'FZShuTi',\n",
       " 'Droid Serif',\n",
       " 'Roboto Slab',\n",
       " 'Wingdings 2',\n",
       " 'Roboto Condensed',\n",
       " 'Microsoft New Tai Lue',\n",
       " 'Garamond',\n",
       " 'Cambria',\n",
       " 'Poiret One',\n",
       " 'Courier New',\n",
       " 'NumberOnly',\n",
       " 'Droid Serif',\n",
       " 'Javanese Text',\n",
       " 'Georgia',\n",
       " 'Leelawadee UI',\n",
       " 'Nirmala UI',\n",
       " 'FZYaoTi',\n",
       " 'Papyrus',\n",
       " 'MS Reference Specialty',\n",
       " 'Kristen ITC',\n",
       " 'Candara',\n",
       " 'Roboto Condensed',\n",
       " 'Bookman Old Style',\n",
       " 'Book Antiqua',\n",
       " 'Nirmala UI',\n",
       " 'STXihei',\n",
       " 'Indie Flower',\n",
       " 'Roboto Condensed',\n",
       " 'Book Antiqua',\n",
       " 'Segoe UI',\n",
       " 'Segoe Script',\n",
       " 'Gabriola',\n",
       " 'Candara',\n",
       " 'Century Gothic',\n",
       " 'Book Antiqua',\n",
       " 'Garamond',\n",
       " 'STKaiti',\n",
       " 'Gabriola',\n",
       " 'Leelawadee UI',\n",
       " 'Segoe UI',\n",
       " 'Open Sans',\n",
       " 'STCaiyun',\n",
       " 'Arial',\n",
       " 'Bradley Hand ITC',\n",
       " 'Calibri',\n",
       " 'Malgun Gothic',\n",
       " 'Comic Sans MS',\n",
       " 'Century Gothic',\n",
       " 'Corbel',\n",
       " 'Arvo',\n",
       " 'Roboto Slab',\n",
       " 'Arial',\n",
       " 'Segoe Print',\n",
       " 'DengXian',\n",
       " 'DengXian',\n",
       " 'Bookman Old Style',\n",
       " 'Microsoft Tai Le',\n",
       " 'Lucida Handwriting',\n",
       " 'Garamond',\n",
       " 'LiSu',\n",
       " 'Kristen ITC',\n",
       " 'Pristina',\n",
       " 'Wingdings 2',\n",
       " 'Century Gothic',\n",
       " 'STFangsong',\n",
       " 'Nirmala UI',\n",
       " 'Mistral',\n",
       " 'Nirmala UI',\n",
       " 'Consolas',\n",
       " 'Juice ITC',\n",
       " 'Nirmala UI',\n",
       " 'Candara',\n",
       " 'Lobster',\n",
       " 'STLiti',\n",
       " 'Arvo',\n",
       " 'Roboto',\n",
       " 'Wingdings 3',\n",
       " 'Segoe UI',\n",
       " 'Mongolian Baiti',\n",
       " 'Lucida Sans Unicode',\n",
       " 'Corbel',\n",
       " 'Arvo',\n",
       " 'Nirmala UI',\n",
       " 'Roboto Slab',\n",
       " 'FangSong',\n",
       " 'Book Antiqua',\n",
       " 'Monotype Corsiva',\n",
       " 'FZShuTi',\n",
       " 'Raleway',\n",
       " 'Century Gothic',\n",
       " 'Papyrus',\n",
       " 'Raleway',\n",
       " 'Gadugi',\n",
       " 'Freestyle Script',\n",
       " 'Leelawadee UI',\n",
       " 'Monotype Corsiva',\n",
       " 'Candara',\n",
       " 'Candara',\n",
       " 'French Script MT',\n",
       " 'DengXian',\n",
       " 'STSong',\n",
       " 'DengXian',\n",
       " 'Open Sans',\n",
       " 'Lobster']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib.font_manager import FontManager\n",
    "manager = FontManager()\n",
    "[ttf.name for ttf in manager.ttflist]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hXDl0rFpA/dN/JKd4ECPOvw2lXD0+LhHtIFq/krxhE2h8698UeKfT+M7DRFZ/Tsl+p241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\nQgib+P8RThWtIbAJwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ca28a4828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 男1000米跑数据的处理\n",
    "data = pd.cut(male_score['男1000米跑分数'],bins = 3,labels=['慢','中','快']).value_counts()\n",
    "percent = data/data.sum()\n",
    "plt.figure(figsize=(6,6))\n",
    "_ = plt.pie(percent,labels=percent.index,\n",
    "            autopct = '%0.2f%%',\n",
    "            textprops = {'family':'KaiTi',\n",
    "                         'fontsize':20})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J84H3u84hIgAcAnzDdQiXSnbKIhpLTAZeB3TvRJH80Y93gu8110FcKOUR8hWojEXyTRBv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\nUCGLiOQJFbKISJ5QIYuI5In/D8ChbU8x0MInAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ca2f616a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 男引体数据的处理\n",
    "data = pd.cut(male_score['男引体'],bins = 3,labels=['低','中','高']).value_counts()\n",
    "percent = data/data.sum()\n",
    "plt.figure(figsize=(6,6))\n",
    "_ = plt.pie(percent,labels=percent.index,\n",
    "            autopct = '%0.2f%%',\n",
    "            textprops = {'family':'KaiTi',\n",
    "                         'fontsize':20})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对女800米跑、女跳远进行直方图绘制统计各分数段人数，分成4份"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ca2fe0908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(female_score['女800米跑分数'],bins = 4,rwidth= 0.5)\n",
    "_ = plt.xticks([12.5,37.5,62.5,87.5])\n",
    "plt.show()\n",
    "# 数据分成四份 0~25，25~50，50~75，75~100\n",
    "# 看图可知 75~ 100分的女生人数最多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YP6J1SZJmMKqgXwoc6Zk/2rVJkn7FFuzD2CSbgE3d7JNJDi5ULdO4Evj+vL/qezPvL3ke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\n6CWpcQa9JDXu/wCXO4o2fZfkOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ca30e7dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(female_score['女跳远分数'],bins = 4,rwidth= 0.5)\n",
    "_ = plt.xticks([12.5,37.5,62.5,87.5])\n",
    "plt.show()\n",
    "# 数据分成四份 0~25，25~50，50~75，75~100\n",
    "# 看图可知，跳远成绩 50~75分的女生人数最多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用嵌套饼图对比男女生体重指数进行比例统计，分为正常、低体重、超重、肥胖"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "正常     0.766094\n",
       "超重     0.124464\n",
       "肥胖     0.081545\n",
       "低体重    0.027897\n",
       "Name: BMI, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_BMI = pd.cut(male_score['BMI'].round(1), # 保留一位小数\n",
    "                 bins = [0,16.4,23.2,26.3,100],\n",
    "                 labels=['低体重','正常','超重','肥胖']).value_counts()\n",
    "male_BMI_percent = male_BMI/male_BMI.sum()\n",
    "male_BMI_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "正常     0.759386\n",
       "超重     0.141638\n",
       "肥胖     0.081911\n",
       "低体重    0.017065\n",
       "Name: BMI, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_BMI = pd.cut(female_score['BMI'].round(1),#保留一位小数\n",
    "                 bins = [0,16.4,22.7,25.2,100],\n",
    "                 labels=['低体重','正常','超重','肥胖']).value_counts()\n",
    "female_BMI_percent = female_BMI/female_BMI.sum()\n",
    "female_BMI_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lHxRnY7sXhuQoE6bfMFQZmBotZi8vY4sWl8DvVegMoeRXTdUu/PjvbDGtVwwu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\naBAREZGS/H/CytzB24JyCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ca40edac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "plt.pie(female_BMI_percent,radius=1,\n",
    "        autopct='%0.2f%%',\n",
    "        pctdistance=0.85,\n",
    "        labels = ['低体重','正常','超重','肥胖'],\n",
    "        wedgeprops={'linewidth':1,# 间隔的宽度\n",
    "                    'width':0.3, # 饼图的宽度\n",
    "                    'edgecolor':'white'},# 间隔的颜色\n",
    "        textprops={'family':'Kaiti','fontsize':18})\n",
    "_ = plt.pie(male_BMI_percent,\n",
    "        radius=0.7,\n",
    "        autopct='%0.2f%%',\n",
    "        pctdistance=0.1,\n",
    "        wedgeprops={'linewidth':1,# 间隔的宽度\n",
    "                    'width':0.7, # 饼图的宽度\n",
    "                    'edgecolor':'white'})# 间隔的颜色\n",
    "\n",
    "plt.show()"
   ]
  }
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